Ultra-low dose naltrexone enhances cannabinoid-induced antinociception
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Both opioids and cannabinoids have inhibitory effects at micromolar doses, which are mediated by activated receptors coupling to Gi/o-proteins. Surprisingly, the analgesic effects of opioids are enhanced by ultra-low doses (nanomolar to picomolar) of the opioid antagonist, naltrexone. As opioid and cannabinoid systems interact, this study investigated whether ultra-low dose naltrexone also influences cannabinoid-induced antinociception. Separate groups of Long-Evans rats were tested for antinociception following an injection of vehicle, a sub-maximal dose of the cannabinoid agonist WIN 55 212-2, naltrexone (an ultra-low or a high dose) or a combination of WIN 55 212-2 and naltrexone doses. Tail-flick latencies were recorded for 3 h, at 10-min intervals for the first hour, and at 15-min intervals thereafter. Ultra-low dose naltrexone elevated WIN 55 212-2-induced tail flick thresholds without extending its duration of action. This enhancement was replicated in animals receiving intraperitoneal or intravenous injections. A high dose of naltrexone had no effect on WIN 55 212-2-induced tail flick latencies, but a high dose of the cannabinoid 1 receptor antagonist SR 141716 blocked the elevated tail-flick thresholds produced by WIN 55 212-2+ultra-low dose naltrexone. These data suggest a mechanism of cannabinoid-opioid interaction whereby activated opioid receptors that couple to Gs-proteins may attenuate cannabinoid-induced antinociception and/or motor functioning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it